Scholarlink

Online grooming, where predators exploit children through digital platforms, presents a growing security challenge with significantly increasing offences. Law enforcement faces substantial detection barriers, requiring manual analysis of hundreds of conversations through slow, inefficient processes limiting scalability. We used an iterative co-design approach to develop an interactive AI platform automating tactics-based grooming detection. Our research identified tensions among stakeholders (law enforcement officers, linguists, and ML experts), resolved conflicting expectations, and produced usable designs aligned with user needs.

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Co-design of an Interactive AI Platform for Tactics-Based Detection of Online Grooming

  • 会议论文

  • 发表日期：

    2026年

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  • Peter Daish,
  • Nicholas Micallef,
  • Nuria Lorenzo-Dus,
  • Adeline Paiement,
  • Deepak Sahoo

摘要

Online grooming, where predators exploit children through digital platforms, presents a growing security challenge with significantly increasing offences. Law enforcement faces substantial detection barriers, requiring manual analysis of hundreds of conversations through slow, inefficient processes limiting scalability. We used an iterative co-design approach to develop an interactive AI platform automating tactics-based grooming detection. Our research identified tensions among stakeholders (law enforcement officers, linguists, and ML experts), resolved conflicting expectations, and produced usable designs aligned with user needs.